Support read-only numpy array.

[MarcCote]

Right now, using Cython version of length and set_number_of_points on read-only numpy arrays (as provided by NiBabel) will fail. The reason is memoryview can only be used with writable numpy array (for more details check https://mail.python.org/pipermail/cython-devel/2013-February/003394.html)

This PR makes sure those Cython functions set the writeable flag of numpy arrays to True before processing them.

[Garyfallidis]

Thx!

[MrBago]

Why does it seem acceptable to change the read status of an argument? At minimum the read status should be changed back once the length computed. There is no guarantee that the read status can safely be changed, read only arrays are often read only for a reason.

I would reject (revert at this point) this PR unless it's meant to address a specific use case.

[MarcCote]

We definitely want to use those two functions even though numpy arrays are readonly since we only reads from them by using memoryviews. I agree it is cleaner to change back the writeable flag.

[MrBago]

Sorry I don't follow, could you clarify?

On Wed, Oct 1, 2014 at 11:43 AM, Marc-Alexandre Côté <
notifications@github.com> wrote:

We definitely want to use those two functions even though numpy arrays are
readonly since we only reads from it but using memoryviews. I agree it is
cleaner to change back the writeable flag.

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Reply to this email directly or view it on GitHub
#427 (comment).

[MarcCote]

Sorry, I meant that because memoryviews accept only writeable numpy array, we have to change the flag in order to use these functions: length or set_number_of_points. I made the changes in my branch to changed the writeable flag back to its original state.

How should I proceed to include those changes: make a new PR right now or revert this one first than make a new PR?

[Garyfallidis]

PR is reverted until we figure out if the streamlines were read only for a reason.

[Garyfallidis]

Sorry @MarcCote you will have to redo this PR. Let's go with the safe way of returning the input with the same state as it was given. @matthew-brett do you know why they streamlines are coming out from nibabel as readonly and do you think that there would be a problem with changing their status?

[MrBago]

Why are you getting read only streamlines?
On Oct 1, 2014 11:54 AM, "Marc-Alexandre Côté" notifications@github.com
wrote:

Sorry, I meant that because of memoryviews accept only writeable numpy
array, we have to change the flag in order to use functions length or
set_number_of_points. I made the changes in my branch to changed the
writeable flag back to its original state.

How should I proceed to include those changes: make a new PR right now or
revert this one first than make a new PR?

—
Reply to this email directly or view it on GitHub
#427 (comment).

[MarcCote]

In NiBabel when reading streamlines from a trk. More specifically, when using np.ndarray with buffer= argument.

import numpy as np
A = np.array([1, 2, 3])
s = A.tostring()
B = np.ndarray(shape=(3,), dtype="int64", buffer=s)
print A.flags, "\n\n", B.flags

will produces this:

  C_CONTIGUOUS : True
  F_CONTIGUOUS : True
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False 

  C_CONTIGUOUS : True
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : False
  ALIGNED : False
  UPDATEIFCOPY : False